Anomaly Detection in Search Queries with Python

Anomaly detection in search queries refers to the process of identifying unusual or unexpected patterns in search query data

Search queries represent the queries made by users in a search engine or a database to retrieve specific information. Anomalies in search queries can indicate various issues such as technical glitches, spam attacks, changes in user behavior, or emerging trends.

To perform anomaly detection in search queries , we can follow these process:

  1. Data Collection:
    • Gather historical data on search queries. We have Google Search Console data (search queries)
  1. Data Preprocessing:

    • Clean the data: Remove any irrelevant or duplicate entries, handle missing values, and perform any necessary data cleaning tasks.
    • Feature engineering: Extract relevant features from the search query data that may help in detecting anomalies. This could include features such as query frequency, length of queries etc.
  2. Exploratory Data Analysis (EDA):

    • Analyze the distribution of search queries over time.
    • Identify patterns and trends in the data.
    • Visualize the data to gain insights into potential anomalies.
  3. Model Selection:

    • Choose an appropriate anomaly detection algorithm. Common approaches include statistical methods (e.g., Z-score, moving averages), machine learning models. I have used isolation forests
    • Consider the characteristics of your data and the specific requirements of your use case when selecting a model.
  4. Anomaly Detection:

    • Apply the trained model to detect anomalies in the test data.
    • Generate anomaly scores or labels for each search query.
  5. Evaluation:

    • Evaluate the performance of the anomaly detection model using appropriate metrics such as precision, recall, F1-score, or area under the ROC curve (AUC-ROC).
    • Adjust parameters or consider alternative models if necessary to improve performance.

Importing necessary libraries

1.Data Collection

Read data from CSV file

2.Data Preprocessing

Top queries with Position' 1.00

3. Exploratory Data Analysis (EDA)

Word Cloud

In scientific notation, "micro-" denotes a factor of 10−610−6. So, for example, 200μ would be equivalent to 200×10−6200×10−6 or 0.0002.

In the correlation matrix, we observe the following relationships between key metrics:

  1. Clicks and Impressions: Positively correlated. This suggests that as the number of Impressions increases, there tends to be more Clicks. It indicates a direct relationship between the two variables.
  1. Clicks and CTR (Click-Through Rate): Weakly positively correlated. This implies that there might be a slight increase in the Click-Through Rate when there are more Clicks. While the correlation is positive (0.25), it's weak, suggesting that the relationship may not be very strong.
  1. Clicks and Position: Weakly negatively correlated. This suggests that higher ad or page Positions may result in fewer Clicks. While this correlation is negative (-0.13), it's weak, meaning that the relationship might not be very strong.
  1. Impressions and CTR: Negatively correlated(-0.04) . This indicates that as the number of Impressions increases, the Click-Through Rate tends to decrease. It suggests an inverse relationship between the two variables.
  1. Impressions and Position: Positively correlated. This implies that ads or pages in higher Positions tend to receive more Impressions. It indicates a direct relationship where higher Positions lead to more Impressions.
  1. CTR and Position: Strongly negatively correlated. This means that higher Positions result in lower Click-Through Rates. It suggests a strong inverse relationship between the two variables. In other words, as the position of a search result increases (meaning it appears lower in the search results), the CTR tends to decrease slightly.

These interpretations provide valuable insights into the relationships between the variables in your dataset, helping you understand how changes in one variable may affect another.

Model Selection

Detecting Anomalies in Search Queries

Now, let’s detect anomalies in search queries. You can use various techniques for anomaly detection. A simple and effective method is the Isolation Forest algorithm, which works well with different data distributions and is efficient with large datasets:

Some key observations and potential anomalies in search queries are as follows:

Low Click-Through Rates (CTRs):

lotus healthcare: This query receives a high number of impressions (2028) but a low CTR (0.0562). This indicates potential issues with the landing page content or ad targeting, as many users see the ad but few click through.

groom facial package: Similar to the above, this query has a high impression count (1495) but a low CTR (0.0140). Investigate the landing page and targeting for this query to improve its effectiveness.

lotus clinic: Although not as drastic, this query also has a low CTR (0.0044) despite a decent number of impressions (2526). Further analysis is needed to understand the cause.

High CTRs:

bride groom facial package: This query has a relatively high CTR (0.1006) compared to others. Understanding what resonates with users here could inform broader content and targeting strategies.

aesthetic spa, ayurvedic treatment, diabetologist pune: These queries with perfect CTRs (1.0000) and low impressions might be specific searches with high intent, but their limited data points require further investigation for confirmation.

Other Points:

lotus healthcare variations: Multiple variations of "lotus healthcare" appear in the search.

diabetes reversal program: This query has a very low CTR (0.0016) despite a significantly high number of impressions (6281).Might be Issue with content. Analyze user behavior and optimize accordingly.

Summary

Search queries are goldmines of information, but not all queries perform the same. Search query anomaly detection unlocks this potential by identifying outliers in performance metrics like clicks, impressions, and click-through rates (CTRs).

This helps businesses:

Catch issues early: Identify queries with unexpectedly low CTRs (potentially underperforming content) or high impressions but low clicks (indicating targeting problems).

Discover opportunities: Spot queries with surprisingly high CTRs (potential content hits) or growing impressions (trending topics).

Optimize content and advertising: Use these insights to improve content relevance, ad targeting, and overall search performance.

Overall, anomaly detection in search queries provides valuable insights to identify underperforming areas, uncover hidden opportunities, and optimize your search strategy for better results.